1. Field of the Invention
The present invention relates to the computer-aided analysis of digitized images and, in particular, to the location of possible anomalies in medical diagnostic and/or industrial images.
2. Description of the Related Art
It has become an established and wide-spread medical practice to perform mammograms to determine the possible presence of breast cancer or other pathological conditions. The amount of time spent by health care professionals in scanning a large number of screening exam images tends to be very short and statistics indicate that diagnostic accuracy may suffer. Also, some features of these radiological images are such that their significance can only be recognized by specialized experts.
While diagnostic uncertainty may not exist in those instances in which a tumor or cancerous tissue is well developed and, thus, clearly visible to all, it will be appreciated that in the very early stages of the development of such tissue abnormalities, a reliable diagnosis based upon a brief examination of radiological images is much more difficult. Yet early diagnosis is a very important factor in arresting and effectively treating cancerous tissues and tissues which exhibit conditions precursory to cancer, particularly breast cancer.
In addition to the benefits of early determination of pathology in medical diagnostic images, such as mammograms, many types of defects in industrial images must also be detected during product manufacture to prevent catastrophic failure, e.g. in airplane turbine blades. Many types of anomalies in industrial materials have been identified and classified by computer analysis of an X-ray image. For example, in some cases, metal cracks or incomplete welds have precisely identifiable characteristics which may be programmed into a standard algorithmic computer for automated analysis. However, in most cases they cannot. Still other types of defects, such as corrosion pits and delaminations in honeycombed or layered materials, are even more difficult to characterize due to their variety of manifestations.
Both the medical diagnostic images and the latter variety of industrial x-ray images are not easily and simply classifiable and do not lend themselves to precise programmable characterizations, such as an expert system, Conventional algorithmic computers excel in applications where the knowledge can be readily represented by means of an explicit set of rules, e.g. a decision tree. Where this is not the case, conventional computers encounter difficulty. While conventional computers can execute an algorithm much more rapidly than any human, they are challenged to match human performance in nonalgorithmic tasks such as visual pattern recognition.
However, parallel distributed processing networks, also known as "neural networks", have been shown to be useful in recognizing patterns in a number of applications involving multiple variables whose precise interactions are not well-understood or quantifiable.
In contrast to algorithmic computer systems (including so-called "expert systems"), a neural net computing system is not formulated to exhibit an explicit algorithm or a set of explicit rules. Instead, a neural network is "trained" to recognize patterns in input data by an iterative adjustment of the connection weights associated with each processing element. These connection weights are adjusted to minimize a preselected output error function. This adjustment of the weights may be accomplished by various well-known techniques.
In traditional image analysis (and/or processing), background subtraction and image normalization have been used to obtain better image resolution with reduced noise. However, when at least some of the areas of interest in the data are sufficiently close to the noise, removal of background from the entire image and subsequent normalization may not satisfactorily distinguish such data from the overall noise. There is thus a need in image analysis for an improvement in the separation of various areas of interest from the effects of overall noise in the image.